Intel Threading Building Blocks

Outfitting C++ for Multi-Core Processor Parallelism

de James Reinders

Public visé :
Débutant

Résumé de l'éditeur

More than ever, multithreading is a requirement for good performance of systems with multi-core chips. This guide explains how to maximize the benefits of these processors through a portable C++ library that works on Windows, Linux, Macintosh, and Unix systems. With it, you'll learn how to use Intel Threading Building Blocks (TBB) effectively for parallel programming -- without having to be a threading expert.

Multi-core chips from Intel and AMD offer a dramatic boost in speed and responsiveness, and plenty of opportunities for multiprocessing on ordinary desktop computers. But they also present a challenge: More than ever, multithreading is a requirement for good performance. This guide explains how to maximize the benefits of these processors through a portable C++ library that works on Windows, Linux, Macintosh, and Unix systems. With it, you'll learn how to use Intel Threading Building Blocks (TBB) effectively for parallel programming -- without having to be a threading expert.

Written by James Reinders, Chief Evangelist of Intel Software Products, and based on the experience of Intel's developers and customers, this book explains the key tasks in multithreading and how to accomplish them with TBB in a portable and robust manner. With plenty of examples and full reference material, the book lays out common patterns of uses, reveals the gotchas in TBB, and gives important guidelines for choosing among alternatives in order to get the best performance.

You'll learn how Intel Threading Building Blocks:

Enables you to specify tasks instead of threads for better portability, easier programming, more understandable source code, and better performance and scalability in general

Focuses on the goal of parallelizing computationally intensive work to deliver high-level solutions

Is compatible with other threading packages, and doesn't force you to pick one package for your entire program

Emphasizes scalable, data-parallel programming, which allows program performance to increase as you add processors

Relies on generic programming, which enables you to write the best possible algorithms with the fewest constraints

Any C++ programmer who wants to write an application to run on a multi-core system will benefit from this book. TBB is also very approachable for a C programmer or a C++ programmer without much experience with templates. Best of all, you don't need experience with parallel programming or multi-core processors to use this book.

More than ever, multithreading is a requirement for good performance of systems with multi-core chips. This guide explains how to maximize the benefits of these processors through a portable C++ library that works on Windows, Linux, Macintosh, and Unix systems. With it, you'll learn how to use Intel Threading Building Blocks (TBB) effectively for parallel programming -- without having to be a threading expert.

Multi-core chips from Intel and AMD offer a dramatic boost in speed and responsiveness, and plenty of opportunities for multiprocessing on ordinary desktop computers. But they also present a challenge: More than ever, multithreading is a requirement for good performance. This guide explains how to maximize the benefits of these processors through a portable C++ library that works on Windows, Linux, Macintosh, and Unix systems. With it, you'll learn how to use Intel Threading Building Blocks (TBB) effectively for parallel programming -- without having to be a threading expert.

Written by James Reinders, Chief Evangelist of Intel Software Products, and based on the experience of Intel's developers and customers, this book explains the key tasks in multithreading and how to accomplish them with TBB in a portable and robust manner. With plenty of examples and full reference material, the book lays out common patterns of uses, reveals the gotchas in TBB, and gives important guidelines for choosing among alternatives in order to get the best performance.

You'll learn how Intel Threading Building Blocks:

Enables you to specify tasks instead of threads for better portability, easier programming, more understandable source code, and better performance and scalability in general

Focuses on the goal of parallelizing computationally intensive work to deliver high-level solutions

Is compatible with other threading packages, and doesn't force you to pick one package for your entire program

Emphasizes scalable, data-parallel programming, which allows program performance to increase as you add processors

Relies on generic programming, which enables you to write the best possible algorithms with the fewest constraints

Any C++ programmer who wants to write an application to run on a multi-core system will benefit from this book. TBB is also very approachable for a C programmer or a C++ programmer without much experience with templates. Best of all, you don't need experience with parallel programming or multi-core processors to use this book.

The Art of Concurrency

A Thread Monkey's Guide to Writing Parallel Applications

de Clay Breshears

Public visé :
Intermédiaire

Résumé de l'éditeur

Looking to take full advantage of multi-core processors with concurrent programming ? As one of the few resources to focus on implementing algorithms in the shared-memory model of multi-core processors, rather than just on theoretical models or distributed-memory architectures, The Art of Concurrency provides the knowledge and hands-on experience you need. You'll get detailed explanations and usable samples to help you transform algorithms from serial to parallel code, along with advice and analysis to steer you clear of mistakes.

Looking to take full advantage of multi-core processors with concurrent programming ? As one of the few resources to focus on implementing algorithms in the shared-memory model of multi-core processors, rather than just on theoretical models or distributed-memory architectures, The Art of Concurrency provides the knowledge and hands-on experience you need. You'll get detailed explanations and usable samples to help you transform algorithms from serial to parallel code, along with advice and analysis to steer you clear of mistakes.

The Art of Multiprocessor Programming

de Maurice Herlihy et Nir Shavit

Public visé :
Débutant

Résumé de l'éditeur

Revised and updated with improvements conceived in parallel programming courses, The Art of Multiprocessor Programming is an authoritative guide to multicore programming. It introduces a higher level set of software development skills than that needed for efficient single-core programming. This book provides comprehensive coverage of the new principles, algorithms, and tools necessary for effective multiprocessor programming. Students and professionals alike will benefit from thorough coverage of key multiprocessor programming issues. This revised edition incorporates much-demanded updates throughout the book, based on feedback and corrections reported from classrooms since 2008 Learn the fundamentals of programming multiple threads accessing shared memory Explore mainstream concurrent data structures and the key elements of their design, as well as synchronization techniques from simple locks to transactional memory systems Visit the companion site and download source code, example Java programs, and materials to support and enhance the learning experience

Revised and updated with improvements conceived in parallel programming courses, The Art of Multiprocessor Programming is an authoritative guide to multicore programming. It introduces a higher level set of software development skills than that needed for efficient single-core programming. This book provides comprehensive coverage of the new principles, algorithms, and tools necessary for effective multiprocessor programming. Students and professionals alike will benefit from thorough coverage of key multiprocessor programming issues. This revised edition incorporates much-demanded updates throughout the book, based on feedback and corrections reported from classrooms since 2008 Learn the fundamentals of programming multiple threads accessing shared memory Explore mainstream concurrent data structures and the key elements of their design, as well as synchronization techniques from simple locks to transactional memory systems Visit the companion site and download source code, example Java programs, and materials to support and enhance the learning experience

An Introduction to Parallel Programming

de Peter Pacheco

Public visé :
Débutant

Résumé de l'éditeur

Author Peter Pacheco uses a tutorial approach to show students how to develop effective parallel programs with MPI, Pthreads, and OpenMP. The first undergraduate text to directly address compiling and running parallel programs on the new multi-core and cluster architecture, An Introduction to Parallel Programming explains how to design, debug, and evaluate the performance of distributed and shared-memory programs. User-friendly exercises teach students how to compile, run and modify example programs. Key features: Takes a tutorial approach, starting with small programming examples and building progressively to more challenging examples Focuses on designing, debugging and evaluating the performance of distributed and shared-memory programs Explains how to develop parallel programs using MPI, Pthreads, and OpenMP programming models

Author Peter Pacheco uses a tutorial approach to show students how to develop effective parallel programs with MPI, Pthreads, and OpenMP. The first undergraduate text to directly address compiling and running parallel programs on the new multi-core and cluster architecture, An Introduction to Parallel Programming explains how to design, debug, and evaluate the performance of distributed and shared-memory programs. User-friendly exercises teach students how to compile, run and modify example programs. Key features: Takes a tutorial approach, starting with small programming examples and building progressively to more challenging examples Focuses on designing, debugging and evaluating the performance of distributed and shared-memory programs Explains how to develop parallel programs using MPI, Pthreads, and OpenMP programming models